{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 运行课程提供代码如下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /home/zjy/深度学习作业/mnist_dataset/train-images-idx3-ubyte.gz\n",
      "Extracting /home/zjy/深度学习作业/mnist_dataset/train-labels-idx1-ubyte.gz\n",
      "Extracting /home/zjy/深度学习作业/mnist_dataset/t10k-images-idx3-ubyte.gz\n",
      "Extracting /home/zjy/深度学习作业/mnist_dataset/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2 as cv\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# %matplotlib inline\n",
    "\n",
    "tf.logging.set_verbosity(tf.logging.INFO)\n",
    "mnist = input_data.read_data_sets(\"/home/zjy/深度学习作业/mnist_dataset/\")\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "for idx in range(16):\n",
    "    plt.subplot(4, 4, idx + 1)\n",
    "    plt.axis('off')\n",
    "    plt.title('[{}]'.format(mnist.train.labels[idx]))\n",
    "    plt.imshow(mnist.train.images[idx].reshape((28, 28)))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 100 training steps, the loss is 0.398498, the validation accuracy is 0.8732\n",
      "after 200 training steps, the loss is 0.155981, the validation accuracy is 0.9104\n",
      "after 300 training steps, the loss is 0.0504329, the validation accuracy is 0.9274\n",
      "after 400 training steps, the loss is 0.123053, the validation accuracy is 0.9314\n",
      "after 500 training steps, the loss is 0.344752, the validation accuracy is 0.9366\n",
      "after 600 training steps, the loss is 0.232483, the validation accuracy is 0.9354\n",
      "after 700 training steps, the loss is 0.27613, the validation accuracy is 0.9438\n",
      "after 800 training steps, the loss is 0.163065, the validation accuracy is 0.9446\n",
      "after 900 training steps, the loss is 0.25869, the validation accuracy is 0.9506\n",
      "the training is finish!\n",
      "the test accuarcy is: 0.9435\n"
     ]
    }
   ],
   "source": [
    "x = tf.placeholder(\"float\", [None, 784])\n",
    "y = tf.placeholder(\"int64\", [None])\n",
    "learning_rate = tf.placeholder(\"float\")\n",
    "\n",
    "\n",
    "def initialize(shape, stddev=0.1):\n",
    "    return tf.truncated_normal(shape, stddev=0.1)\n",
    "\n",
    "\n",
    "L1_units_count = 100\n",
    "\n",
    "W_1 = tf.Variable(initialize([784, L1_units_count]))\n",
    "b_1 = tf.Variable(initialize([L1_units_count]))\n",
    "logits_1 = tf.matmul(x, W_1) + b_1\n",
    "output_1 = tf.nn.relu(logits_1)\n",
    "\n",
    "L2_units_count = 10\n",
    "W_2 = tf.Variable(initialize([L1_units_count, L2_units_count]))\n",
    "b_2 = tf.Variable(initialize([L2_units_count]))\n",
    "logits_2 = tf.matmul(output_1, W_2) + b_2\n",
    "\n",
    "logits = logits_2\n",
    "\n",
    "cross_entropy_loss = tf.reduce_mean(\n",
    "    tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(\n",
    "    learning_rate=learning_rate).minimize(cross_entropy_loss)\n",
    "\n",
    "pred = tf.nn.softmax(logits)\n",
    "correct_pred = tf.equal(tf.argmax(pred, 1), y)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
    "\n",
    "batch_size = 32\n",
    "trainig_step = 1000\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    # 定义验证集与测试集\n",
    "    validate_data = {\n",
    "        x: mnist.validation.images,\n",
    "        y: mnist.validation.labels,\n",
    "    }\n",
    "    test_data = {x: mnist.test.images, y: mnist.test.labels}\n",
    "\n",
    "    for i in range(trainig_step):\n",
    "        xs, ys = mnist.train.next_batch(batch_size)\n",
    "        _, loss = sess.run(\n",
    "            [optimizer, cross_entropy_loss],\n",
    "            feed_dict={\n",
    "                x: xs,\n",
    "                y: ys,\n",
    "                learning_rate: 0.3\n",
    "            })\n",
    "\n",
    "        # 每100次训练打印一次损失值与验证准确率\n",
    "        if i > 0 and i % 100 == 0:\n",
    "            validate_accuracy = sess.run(accuracy, feed_dict=validate_data)\n",
    "            print(\n",
    "                \"after %d training steps, the loss is %g, the validation accuracy is %g\"\n",
    "                % (i, loss, validate_accuracy))\n",
    "            saver.save(sess, './model_1/model_trained.ckpt', global_step=i)\n",
    "\n",
    "    print(\"the training is finish!\")\n",
    "    # 最终的测试准确率\n",
    "    acc = sess.run(accuracy, feed_dict=test_data)\n",
    "    print(\"the test accuarcy is:\", acc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from /home/zjy/文档/pycharmprojects/深度学习/第七周作业/model_1/model_trained.ckpt-900\n",
      "0.96\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    ckpt = tf.train.get_checkpoint_state('/home/zjy/文档/pycharmprojects/深度学习/第七周作业/model_1/')\n",
    "    if ckpt and ckpt.model_checkpoint_path:\n",
    "        saver.restore(sess, ckpt.model_checkpoint_path)\n",
    "        final_pred, acc = sess.run(\n",
    "            [pred, accuracy],\n",
    "            feed_dict={\n",
    "                x: mnist.test.images[:25],\n",
    "                y: mnist.test.labels[:25]\n",
    "            })\n",
    "        orders = np.argsort(final_pred)\n",
    "        plt.figure(figsize=(10, 10))\n",
    "        print(acc)\n",
    "        for idx in range(25):\n",
    "            order = orders[idx, :][-1]\n",
    "            prob = final_pred[idx, :][order]\n",
    "            plt.subplot(5, 5, idx + 1)\n",
    "            plt.axis('off')\n",
    "            plt.title('{}: [{}]-[{:.1f}%]'.format(mnist.test.labels[idx],\n",
    "                                                  order, prob * 100))\n",
    "            plt.imshow(mnist.test.images[idx].reshape((28, 28)))\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    else:\n",
    "        pass\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 对模型结构的理解\n",
    "#### 对于模型结构我的理解是：\n",
    "1. 对于单层的神经网络，它含有输入层、一个隐层和输出层，而感知机和单层神经网络的区别就在于激活函数由阶跃函数换成非线性函数。\n",
    "2. 对于本程序，所用的是两层的神经网络，也就是包含两个隐层，在结构方面，所有同一隐层的神经元都与上一层的每个输出相连，同一隐层的神经元之间不相互连接。\n",
    "3. 对于每一个神经元与前一层的输入之间都附加一个权重。\n",
    "4. 在编写程序时，对于需要更新参数的变量，在定义的时候需要用tf.Variable(),比如权重参数和偏置;而对于输入x和标签y在定义的时候用占位符tf.placeholder()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 对模型训练过程（梯度如何计算，参数如何更新）的理解\n",
    "#### 训练过程的理解如下：\n",
    "1. 对于神经网络采用的损失函数为交叉熵损失，参数更新的方法为梯度下降。参数调整的算法为反向传播算法，核心是通过比较输出y和真值t，对参与计算的w进行调整。\n",
    "2. 梯度计算的过程:梯度的计算是通过对每个权重进行求偏导数，然后在负梯度方向对权重参数进行更新，在偏导的求解过程中，最核心的思想是通过链式法则。核心计算公式如左图所示:\n",
    "3. 参数更新采用的是负梯度方向，用之前的参数减学习率乘以损失函数对该权重参数的偏导，参数更新公式如右图所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2 as cv\n",
    "from matplotlib import font_manager # fname中选择一个你本机查询出来的字体 若没有中文字体则需要你本人手动安装 \n",
    "font = font_manager.FontProperties(fname=\"/usr/share/fonts/TTF/DroidSansFallbackFull.ttf\")\n",
    "import matplotlib.pyplot as plt\n",
    "img_1 = cv.imread(\"/home/zjy/图片/BP公式.png\")\n",
    "img_2 = cv.imread(\"/home/zjy/图片/参数更新公式.png\")\n",
    "\n",
    "plt.figure(figsize=(16,16))\n",
    "plt.subplot(1,2,1),plt.title(\"BP core formula\")\n",
    "plt.imshow(img_1)\n",
    "plt.subplot(1,2,2),plt.title('Example of parameter update')\n",
    "plt.imshow(img_2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BP1：用来计算神经网络输出层最终的损失产生的梯度。BP2：表示输出层的梯度向前面每一层进行传播的一个过程，注意里面的求和符号。\n",
    "BP3：是梯度向偏置进行传播的过程。 BP4：是损失函数对权重参数的偏导公式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 对计算图的理解"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    计算图主要由四个部分构成：1、张量（Tensor） 2、操作（Operation） 3、变量（Variable） 4、会话（Session）\n",
    "    其中，变量是在优化运算过程中可以进行更新的，被定义成变量Variable，比如权重参数和偏置，而像输入特征x则用占位符placeholder()定义。\n",
    "    操作operation，则是专门运算的操作节点，所有操作都是一个op，比如加法，乘法。\n",
    "    而会话Session则是用来运行整个图的。\n",
    "    \n",
    "    首先定义计算图的概念，计算图是有向图，神经网络是其特殊形式, 其中节点对应于操作或变量。变量可以将其值提供给操作，操作可以将其输出提供给其他操作。这样，图中的每个节点都定义了变量的函数。进入节点并从节点出来的值称为张量(多维数组的另一别称),它包含标量，向量和矩阵以及更高等级的张量。“从左向右进行计算”是一种正方向上的传播，简称为正向传播（forward propagation）。正向传播是从计算图出发点到结束点的传播。既然有正向传播这个名称，当然也可以考虑反向（从图上看的话，就是从右向左）的传播。实际上，这种传播称为反向传播（backward propagation）。而传播的是线上的张量！\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 解释这里的模型为什么效果比较差"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型效果差的原因有：\n",
    "1. 神经网络只有两层,层数太少，神经元数目较少。\n",
    "2. 采用的学习率为固定学习率。\n",
    "3. 训练次数不够"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
